




import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


UpperCamelCase__ :   str						=							logging.get_logger(__name__)

UpperCamelCase__ :   str						=							{
    '''ut/deta''': '''https://huggingface.co/ut/deta/resolve/main/config.json''',
}







class 				lowerCAmelCase_       (					lowerCamelCase_							):
				__a     :       Union[str, Any]						=     "deta"
				__a     :       Optional[int]						=     {
				    "hidden_size": "d_model",
				    "num_attention_heads": "encoder_attention_heads",
				}






				def __init__(			self      ,snake_case__=None      ,snake_case__=900      ,snake_case__=2048      ,snake_case__=6      ,snake_case__=2048      ,snake_case__=8      ,snake_case__=6      ,snake_case__=1024      ,snake_case__=8      ,snake_case__=0.0      ,snake_case__=True      ,snake_case__="relu"      ,snake_case__=256      ,snake_case__=0.1      ,snake_case__=0.0      ,snake_case__=0.0      ,snake_case__=0.02      ,snake_case__=1.0      ,snake_case__=True      ,snake_case__=False      ,snake_case__="sine"      ,snake_case__=5      ,snake_case__=4      ,snake_case__=4      ,snake_case__=True      ,snake_case__=300      ,snake_case__=True      ,snake_case__=True      ,snake_case__=1      ,snake_case__=5      ,snake_case__=2      ,snake_case__=1      ,snake_case__=1      ,snake_case__=5      ,snake_case__=2      ,snake_case__=0.1      ,snake_case__=0.25      ,**snake_case__      ,):
							if backbone_config is None:
										logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.' )
										SCREAMING_SNAKE_CASE_   : List[Any]					  =      CONFIG_MAPPING['resnet'](out_features=['stage2', 'stage3', 'stage4'] )
							else:
										if isinstance(snake_case__      ,snake_case__ ):
													SCREAMING_SNAKE_CASE_   : List[str]					  =      backbone_config.pop('model_type' )
													SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      CONFIG_MAPPING[backbone_model_type]
													SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      config_class.from_dict(snake_case__ )

							SCREAMING_SNAKE_CASE_   : str					  =      backbone_config
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      num_queries
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      max_position_embeddings
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      d_model
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      encoder_ffn_dim
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      encoder_layers
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      encoder_attention_heads
							SCREAMING_SNAKE_CASE_   : List[str]					  =      decoder_ffn_dim
							SCREAMING_SNAKE_CASE_   : List[str]					  =      decoder_layers
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      decoder_attention_heads
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      dropout
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      attention_dropout
							SCREAMING_SNAKE_CASE_   : int					  =      activation_dropout
							SCREAMING_SNAKE_CASE_   : Any					  =      activation_function
							SCREAMING_SNAKE_CASE_   : Any					  =      init_std
							SCREAMING_SNAKE_CASE_   : int					  =      init_xavier_std
							SCREAMING_SNAKE_CASE_   : Tuple					  =      encoder_layerdrop
							SCREAMING_SNAKE_CASE_   : Tuple					  =      auxiliary_loss
							SCREAMING_SNAKE_CASE_   : str					  =      position_embedding_type
							# deformable attributes
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      num_feature_levels
							SCREAMING_SNAKE_CASE_   : Any					  =      encoder_n_points
							SCREAMING_SNAKE_CASE_   : Dict					  =      decoder_n_points
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      two_stage
							SCREAMING_SNAKE_CASE_   : str					  =      two_stage_num_proposals
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      with_box_refine
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      assign_first_stage
							if two_stage is True and with_box_refine is False:
										raise ValueError('If two_stage is True, with_box_refine must be True.' )
							# Hungarian matcher
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      class_cost
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      bbox_cost
							SCREAMING_SNAKE_CASE_   : Any					  =      giou_cost
							# Loss coefficients
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      mask_loss_coefficient
							SCREAMING_SNAKE_CASE_   : str					  =      dice_loss_coefficient
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      bbox_loss_coefficient
							SCREAMING_SNAKE_CASE_   : str					  =      giou_loss_coefficient
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      eos_coefficient
							SCREAMING_SNAKE_CASE_   : Tuple					  =      focal_alpha
							super().__init__(is_encoder_decoder=snake_case__      ,**snake_case__ )






				@property
				def 		snake_case	(			self ):
							return self.encoder_attention_heads






				@property
				def 		snake_case	(			self ):
							return self.d_model






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      copy.deepcopy(self.__dict__ )
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      self.backbone_config.to_dict()
							SCREAMING_SNAKE_CASE_   : Any					  =      self.__class__.model_type
							return output




'''simple docstring'''




import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD






def __lowerCAmelCase					(				UpperCamelCase__							)      ->       List[str]:
    def wrapper(*UpperCamelCase__							,    **UpperCamelCase__							):
        __lowerCamelCase            =    timeit.default_timer()
        __lowerCamelCase            =    func(*UpperCamelCase__							,    **UpperCamelCase__							)
        __lowerCamelCase            =    timeit.default_timer() - starttime
        return delta

    __lowerCamelCase            =    func.__name__

    return wrapper






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__=1_00							,    UpperCamelCase__=None							)      ->       Dict:
    __lowerCamelCase            =    []
    __lowerCamelCase            =    seq_shapes or {}
    for i in range(UpperCamelCase__							):
        __lowerCamelCase            =    {}
        for col_id, (k, v) in enumerate(features.items()							):
            if isinstance(UpperCamelCase__							,    _ArrayXD							):
                __lowerCamelCase            =    np.random.rand(*v.shape							).astype(v.dtype							)
            elif isinstance(UpperCamelCase__							,    datasets.Value							):
                if v.dtype == "string":
                    __lowerCamelCase            =    '''The small grey turtle was surprisingly fast when challenged.'''
                else:
                    __lowerCamelCase            =    np.random.randint(10							,    size=1							).astype(v.dtype							).item()
            elif isinstance(UpperCamelCase__							,    datasets.Sequence							):
                while isinstance(UpperCamelCase__							,    datasets.Sequence							):
                    __lowerCamelCase            =    v.feature
                __lowerCamelCase            =    seq_shapes[k]
                __lowerCamelCase            =    np.random.rand(*UpperCamelCase__							).astype(v.dtype							)
            __lowerCamelCase            =    data

        dummy_data.append((i, example)							)

    return dummy_data






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__=1_00							,    UpperCamelCase__=None							)      ->       Dict:
    __lowerCamelCase            =    generate_examples(UpperCamelCase__							,    num_examples=UpperCamelCase__							,    seq_shapes=UpperCamelCase__							)

    with ArrowWriter(features=UpperCamelCase__							,    path=UpperCamelCase__							) as writer:
        for key, record in dummy_data:
            __lowerCamelCase            =    features.encode_example(UpperCamelCase__							)
            writer.write(UpperCamelCase__							)

        __lowerCamelCase      ,    __lowerCamelCase            =    writer.finalize()

    if not num_final_examples == num_examples:
        raise ValueError(
            f"""Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}."""							)

    __lowerCamelCase            =    datasets.Dataset.from_file(filename=UpperCamelCase__							,    info=datasets.DatasetInfo(features=UpperCamelCase__							)							)

    return dataset
